Data Wrangling with Hive: Managing Big Data Efficiently

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Introduction to Spark Runtime Architecture

Covers the Spark runtime architecture, including RDDs, transformations, actions, and caching for performance optimization.

Decision Tree Classification

Covers decision tree classification using KNIME Analytics Platform for data preprocessing and model creation.

Introduction to Data Stream Processing: Concepts and Applications

Covers the principles of data stream processing and its applications in real-time data analysis.

Integrating Scalable Data Storage and Map Reduce Processing with Hadoop

Covers the integration of scalable data storage and map reduce processing using Hadoop, including HDFS, Hive, Parquet, ORC, Spark, and HBase.

Introduction to Spark Runtime Architecture

Introduces Apache Spark, covering its architecture, RDDs, transformations, actions, fault tolerance, deployment options, and practical exercises in Jupyter notebooks.

Spark Data Frames

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Introduction to Database Systems

Covers the basics of database systems, including data modeling, DBMS, data independence, and the course overview.

Data Modeling: Concepts and Applications

Introduces data modeling concepts, SQL usage, and Pandas library applications for efficient data processing.

Data, big data, clouds and IoT

Explores data representation, databases, cloud computing, and challenges in the cloud environment.

Data Science for Engineers: Part 2

Explores data manipulation, exploration, and visualization in data science projects using Python.